Medical image processing device and medical image processing method

ABSTRACT

There is provided a medical image processing device and a medical image processing method that can extract and display lesion candidate regions having different sizes and similarity forms at a time through a series of processing. Therefore, with respect to a first medical image, a first evaluation for the curved surface form of the first medical image is made to extract first lesion candidate regions. With respect to each of the first lesion candidate regions extracted by a first extracting unit, a second evaluation for the curved surface form thereof is made to extract a second lesion candidate region. The second lesion candidate region extracted by a second extracting unit is superimposed and displayed on a second medical image.

TECHNICAL FIELD

The present invention relates to a medical image processing device forextracting and displaying lesion candidates on the basis of a medicalimage.

BACKGROUND ART

Tomographic images, etc. of an examinee which are scanned by an X-ray CT(Computed Tomography) apparatus, an MRI (Magnetic Resonance Imaging)apparatus, an ultrasonic apparatus, etc. have been hitherto known asimages used for medical diagnosis. There has been developed acomputer-aided detection apparatus (Computer-Aided Detection;hereinafter referred to as CAD) in which a medical image as describedabove is analyzed by using a computer to detect lesion candidates fromshade and shadow of the medical image and present the lesion candidatesto a medical doctor. CAD automatically detects an image region estimatedas a lesion site (hereinafter referred to as lesion candidate region) onthe basis of a form characteristic or a density characteristic of thelesion site, and it reduces a labor imposed on the medical doctor.

Furthermore, when a large number of cases are required to be read likehealth check or the like, there is an operator's requirement ofextracting and displaying lesion candidates of plural desired sizes at atime through a series of processing to efficiently perform diagnosis.For example, polyps in a colon region have a characteristic feature, buthave various sizes. In general, lesion candidates as medical treatmenttargets are equal to 5 mm or more in size, and lesion candidates of 10mm or more have a high risk that they become colon cancers. For example,Patent Document 1 discloses a method of extracting lesion candidates bymaking an evaluation using a feature amount representing the form of acurved surface (shape index) for a medical image.

PRIOR ART DOCUMENT Patent Document

-   Patent Document 1: JP-A-2006-230910

SUMMARY OF THE INVENTION Problem to be Solved by the Invention

However, when lesion candidates as extraction targets are different fromone another in size, the optimum value of a parameter for calculatingthe feature amount representing the form (form exponent; for example,shape index) is different among them. Therefore, the conventional methodhas a disadvantage that even lesion candidates representing the sameform cannot be extracted and displayed at a time through a series ofprocessing when they are different from one another in size.

The present invention has been implemented in view of the foregoingproblem, and has an object to provide a medical image processing deviceand a medical image processing method that can extract and displaylesion candidates having similarity forms and different sizes at a timethrough a series of processing.

Means of Solving the Problem

In order to attain the above object, according to a first invention, amedical image processing device for extracting and displaying lesioncandidate regions from a medical image is characterized by comprising: afirst extracting unit that makes a first evaluation of a curved surfaceform for a first medical image to extract a first lesion candidateregion; a second extracting unit that makes a second evaluation of acurved surface form for each first lesion candidate region extracted bythe first extracting unit to extract a second lesion candidate region;and a display unit that displays the second lesion candidate regionextracted by the second extracting unit while the second lesioncandidate region is superimposed on a second medical image.

According to a second invention, a medical image processing method forextracting and displaying lesion candidate regions from a medical imageis characterized by comprising: a first extracting step that makes afirst evaluation of a curved surface form for a first medical image toextract a first lesion candidate region; a second extracting unit thatmakes a second evaluation of a curved surface form for each first lesioncandidate region extracted by a first extracting unit to extract asecond lesion candidate region; and a display unit that displays thesecond lesion candidate region extracted by a second extracting unitwhile the second lesion candidate region is superimposed on a secondmedical image.

Effect of the Invention

According to this invention, there can be provided the medical imageprocessing method and the medical image processing device that canextract and display lesion candidates having similarity forms anddifferent sizes at a time through a series of processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a hardware construction diagram showing the overallconstruction of an image processing system 1.

FIG. 2 shows an example of GUI 2 used when lesion candidate extractionprocessing is executed.

FIG. 3 is a flowchart showing the flow of lesion candidate extractionprocessing in a first embodiment.

FIG. 4 is a diagram showing a form exponent (Shape Index).

FIG. 5 is a diagram showing a differential distance.

FIG. 6 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S107 of FIG. 3.

FIG. 7 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S108 of FIG. 3.

FIG. 8 is a diagram showing calculation of a region size.

FIG. 9 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S113 of FIG. 3.

FIG. 10 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S114 of FIG. 3.

FIG. 11 shows an example of a superimposed image obtained bysuperimposing a lesion candidate region on a panoramic image.

FIG. 12 is a diagram showing a slide display in a hollow organ core linedirection.

FIG. 13 is a flowchart showing the flow of lesion candidate extractionprocessing in a second embodiment.

FIG. 14 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S207 of FIG. 13.

FIG. 15 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S208 of FIG. 13.

FIG. 16 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S213 of FIG. 13.

FIG. 17 is a diagram showing an example of a lesion candidate regionextracted at the stage of step S214 of FIG. 13.

FIG. 18 shows an example of a superimposed image obtained bysuperimposing a lesion candidate region on a virtual endoscopic image.

FIG. 19 is a diagram showing a slide display in a hollow organ core linedirection.

FIG. 20 is a flowchart showing the flow of display processing accordingto a third embodiment.

FIG. 21 shows a display example according to the third embodiment.

FIG. 22 is a flowchart showing the flow of display processing accordingto a fourth embodiment.

FIG. 23 shows an example of a display format of a lesion candidateregion.

FIG. 24 shows an example of the display format of the lesion candidateregion.

FIG. 25 shows an example of the display format of the lesion candidateregion.

BEST MODES FOR CARRYING OUT THE INVENTION

Preferable embodiments according to the present invention will bedescribed in detail with reference to the accompanying drawings.

First Embodiment

First, the construction of an image processing system 1 to which amedical image processing device according to the present invention isapplied will be described.

As shown in FIG. 1, the image processing system 1 includes a medicalimage processing device 100 having a display device 107 and an inputdevice 109, and an image data base 111 and a medical image scanningdevice 112 which are connected to the medical image processing device100 through a network 110.

The medical image processing device 100 is a image diagnosing computerinstalled in a hospital or the like, and it functions as acomputer-aided detection device (CAD) for analyzing a medical image,detecting a lesion candidate (s) from shade and shadow of the medicalimage and presenting the lesion candidate (s) to a medical doctor. Themedical image processing device 100 has CPU 101 (Central ProcessingUnit) 101, a main memory 102, a storage device 103, a communicationinterface (communication I/F) 104, a display memory 105, and aninterface (I/F) 106 with external equipment such as a mouse 108 or thelike, and the respective parts are connected to one another through abus 113.

CPU 101 calls up a program stored in the main memory 102, the storagedevice 103 or the like into a work memory area on RAM of the main memory102 to execute the program, and controls the operation of the respectiveparts connected through the bus 113 to implement various kinds ofprocessing executed by the medical image processing device 100.

Furthermore, CPU 101 executes processing described later concerningextraction of a lesion candidate region in the first embodiment (seeFIG. 3).

The main memory 102 comprises ROM (Read Only Memory), RAM (Random AccessMemory), etc. ROM permanently holds programs such as a boot program ofthe computer, programs such as BIOS, etc., data, etc. RAM temporarilyholds programs loaded from ROM, the storage device 103, etc., data, etc.and has a work area which is used to perform various kinds of processingby CPU 101.

The storage device 103 is a storage device for reading/writing datafrom/into HDD (hard disk drive) or another storage medium, and programsto be executed by CPU 101, data required to execute programs, OS(operating system), etc. are stored in the storage device 103. Withrespect to the programs, a control program corresponding to OS andapplication programs are stored. Program codes of these programs areread out by CPU 101 as occasion demands, shifted to RAM of the mainmemory 102 and executed as various kinds of means.

The communication I/F 104 has a communication control device, acommunication port, etc., and mediates communications with the medicalimage processing device 100 and the network 110. The communication I/F104 controls communication with the image data base 111, anothercomputer or the medical image scanning device 112 through the network110. I/F 106 is a port for connection to peripheral equipment, andtransmits/receives data to/from the peripheral equipment. For example,input devices such as the mouse 108, etc. may be connected through I/F106.

The mouse 108 indicates any position on a display screen by movingoperation or operation of a button, a wheel or the like, and pushes asoftware switch, etc., and outputs the operation signal corresponding tothe operation through I/F 106 to CPU 101. The display memory 105 is abuffer for temporarily accumulating display data input from CPU 101. Theaccumulated display data are output to the display device 107 at apredetermined timing.

The display device 107 comprises a display device such as a liquidcrystal panel, a CRT monitor or the like, and a logic circuit forexecuting display processing in cooperation with the display device, andit is connected to CPU 101 through the display memory 105. Under thecontrol of CPU 101, the display device 107 displays the display dataaccumulated in the display memory 105 on the display device.

The input device 109 is an input device such as a keyboard or the likeand outputs to CPU 101 various kinds of instructions and informationinput by an operator such as ID information for specifying medicalimages, diagnosis reports of medical images displayed on the displaydevice 107, etc., for example. The operator dialogically operates themedical image processing device 100 by using the external equipment suchas the display device 107, the input device 109, the mouse 108, etc.

The network 110 contains various kinds of communication networks such asLAN (Local Area Network), WAN (Wide Area Network), Intranet, Internet,etc., and mediates communication connection between the image data base111, a server, another information equipment or the like and the medicalimage processing device 100.

The image data base 111 accumulates and stores medical images scanned bythe medical image scanning device 112, and it is provided to a server orthe like in a hospital, a medical center or the like. In the imageprocessing system 1 shown in FIG. 1, the image data base 111 isconnected to the medical image processing device 100 through the network110, however, the image data base 111 may be provided to the storagedevice 103 in the medical image processing device 100, for example.

The medical image scanning device 112 is an apparatus for picking uptomographic images of an examinee such as an X-ray CT apparatus, an MRIapparatus, an ultrasonic apparatus, a scintillation camera device, PET(Positron Emission Tomography) apparatus, SPECT (Single Photon EmissionComputed Tomography) apparatus or the like, and it is connected to theimage data base 111 or the medical image processing device 100 throughthe network 110.

Medical images handled by the image processing system 1 of thisinvention contain tomographic images, panoramic images of hollow organsand virtual endoscopic images of examinees. The panoramic image isobtained by displaying the inside of an internal organ so that thehollow organ is developed around the core line of the hollow organ (seeFIG. 11), and the virtual endoscopic image is obtained by displaying theinside of the hollow organ according to a display method based on acentral projection method from a virtual viewing point provided to theinside of the hollow organ (see FIG. 18).

Next, the operation of the image processing system 1 will be describedwith reference to FIGS. 2 to 12.

CPU 101 of the medical image processing device 100 reads out a programconcerning lesion candidate extraction processing and data from the mainmemory 102, and executes the lesion candidate extraction processing onthe basis of this program and the data.

When execution of the following lesion candidate extraction processingis started, it is assumed that image data are taken from the image database 111 or the like through the network 110 and the communication I/F104 and stored into the storage device 103 of the medical imageprocessing device 100. Furthermore, when an execution start instructionof the lesion candidate extraction processing is input from the inputdevice 109 or the like, for example, GUI 2 shown in FIG. 2 is read outfrom the storage device 106 and displayed on the display device 107.

GUI 2 shown in FIG. 2 has various kinds of input frames for inputtingvarious conditions, set values or instruction required when a lesioncandidate region is extracted, and an image display region 7 fordisplaying an extraction result, etc. An operator can dialogically inputvarious conditions, etc. by operating the input device 109, the mouse108 or the like while referring to a content displayed on GUI 2.

On GUI 2 are displayed a data read-in button 3, an input frame 4 forinputting an initial differential distance, an input frame 5 forinputting an initial form exponent threshold value, an input frame 6 forinputting a form exponent threshold value, an image display region 7 fordisplaying various kinds of images such as a medical image as a target,an extraction result of the lesion candidate extraction region, etc., aninput frame 8 for instructing and inputting the size of the lesioncandidate region to be superimposed and displayed, a scroll bar 9 forvarying a value to be input to the input frame 8, etc.

In the lesion candidate extraction processing of FIG. 3, when the dataread-in button 3 of GUI 2 of FIG. 2 is first clicked, CPU 101 executesthe processing of reading image data. CPU 101 displays an imageselection window on the display device 107 so that plural selectiontarget images are displayed in a list or thumb nail display style on theimage selection window, and accepts selection of an image from theoperator. When the operator selects a desired image, CPU 101 reads outselected image data from the storage device 103 and holds the image datainto the main memory 102 (step S101).

In this embodiment, it is assumed that image data of a hollow organregion such as a colon or the like are selected. Furthermore, the imagedata read at this stage are assumed as volume image data obtained bystacking plural tomographic images.

Subsequently, CPU 101 extracts a core line from the image data read instep S101 (step S102). As disclosed in JP-A-2006-42969, the extractionof the core line is performed by tracking a start point, a terminalpoint and passing points indicated in the hollow organ region of thedisplayed volume image data.

Subsequently, CPU 101 creates a display image by using core lineinformation extracted in step S102. In this case, it is assumed that apanoramic image 71 is created as a display image (step S103; see FIG.11). Details of the creation of the panoramic image 71 are disclosed inthe Patent Document (U.S. Pat. No. 3,627,066), and the descriptionthereof is omitted.

Subsequently, CPU 101 sets a parameter P1 for calculating a formexponent S for the overall panoramic image 71 created in step S103 (stepS104). Here, the form exponent S is an index for estimating the state ofthe curved surface of the image, and so-called Shape Index is used as anexample. The form exponent S is represented by the followingmathematical expression (1). The parameter P1 is, for example, adifferential distance for calculating a differential value at a point ofinterest, and used when the form exponent S is calculated (see thefollowing mathematical expression (3)). As the parameter P1, may be useda value which is empirically determined in advance or any numericalvalue input to the input frame 4 of GUI 2 of FIG. 2. CPU 101 stores theset parameter P1 into the main memory 102.

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu}(1)} \right\rbrack & \; \\{S = {\frac{1}{2} - {\frac{1}{\pi}{\arctan\left( \frac{\lambda_{\max} + \lambda_{\min}}{\lambda_{\max} - \lambda_{\min}} \right)}}}} & (1)\end{matrix}$In the mathematical expression (1), λmax, λmin represent the maximumvalue and minimum value of a main curvature at each point on a curvedsurface, and they are calculated by the following mathematicalexpression (2).

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu} 2} \right\rbrack & \; \\{{\lambda_{\max} \equiv {\frac{1}{2}\left\lbrack {f_{xx} + f_{yy} + \sqrt{\left( {f_{xx} + f_{yy}} \right)^{2} - {4\left( {{f_{xx}f_{yy}} - {f_{xy}f_{xy}}} \right)}}} \right\rbrack}}{\lambda_{\min} \equiv {\frac{1}{2}\left\lbrack {f_{xx} + f_{yy} - \sqrt{\left( {f_{xx} + f_{yy}} \right)^{2} - {4\left( {{f_{xx}f_{yy}} - {f_{xy}f_{xy}}} \right)}}} \right\rbrack}}} & (2)\end{matrix}$In the mathematical expression (2), fxx, fyy, fxy represent secondarypartial derivatives of f(x, y) at a pixel-of-interest p, and it iscalculated according to the following mathematical expression (3) byusing the coordinate (x, y) of the pixel-of-interest p and depth dataf(x, y) at the pixel p. The depth data f(x, y) represents the distanceon a three-dimensional coordinate from the surface of a hollow organ tothe core line thereof at a coordinate (x, y) in a real space of eachpoint (each pixel) of the wall of the hollow organ represented as apanoramic image. The depth data f (x, y) is generated when the panoramicimage 71 is created.

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu} 3} \right\rbrack & \; \\{{{fxx} = \frac{{f\left( {{x + {P\; 1}},y} \right)} + {f\left( {{x - {P\; 1}},y} \right)} - {2{f\left( {x,y} \right)}}}{P\; 1^{2}}}{{fyy} = \frac{{f\left( {x,{y + {P\; 1}}} \right)} + {f\left( {x,{y - {P\; 1}}} \right)} - {2{f\left( {x,y} \right)}}}{P\; 1^{2}}}{{fxy} = \frac{\begin{matrix}{{f\left( {{x + {P\; 1}},{y + {P\; 1}}} \right)} - {f\left( {{x - {P\; 1}},{y + {P\; 1}}} \right)} -} \\{{f\left( {{x + {P\; 1}},{y - {P\; 1}}} \right)} + {f\left( {{x - {P\; 1}},{y - {P\; 1}}} \right)}}\end{matrix}}{P\; 1^{2}}}} & (3)\end{matrix}$

As shown in FIG. 4, the Shape Index (form exponent S) has a value whichcontinuously varies from 0 to 1, and different curved surface statescorrespond to the respective values. That is, a concave hemispherecorresponds to a value “0” of shape Index, and the value of Shape Indexrepresents a concaved semicircular column, a saddle-shaped plane/flatplane, a convex semicircular column and a convex hemisphere in thisorder as the value of Shape Index increases from “0”. The convexhemisphere corresponds to the value “1” of Shape Index.

When the form exponent S of a convex surface 601 shown in FIG. 5 isdetermined, the value of the form exponent S is dependent on thedifferential distance (parameter P1). The form exponent S has themaximum value when the differential distance is equal to the same levelas the width of the curved surface (unevenness). When the differentialdistance is smaller than the width of the unevenness as indicated by anarrow 602 of FIG. 5, the form exponent S of a substantially planarsurface is determined, and thus the form exponent S has a value in theneighborhood of “0.5”. On the other hand, when the width of theunevenness and the differential distance are equal to the same level asindicated by an arrow 603 of FIG. 5, the gradient of the convex surfacecan be captured when a secondary derivative function is calculated.Therefore, the form exponent S has a value in the neighborhood of “1”,and thus it represents that the form is close to the convex hemisphere.

As described above, the parameter P1 set in step S104 is used when themain curvature λmax, λmin are calculated, and thus the calculationresult varies in accordance with the value of the parameter P1 even whenthe form exponent S for the same pixel is calculated.

In the lesion candidate extraction processing of the present invention,the form exponent S is repetitively calculated in a series ofprocessing. In the following description, a form exponent S which isfirst calculated (step S106) is called as an initial form exponent S₀,and a form exponent which is calculated at a subsequent stage (stepS112) is called as S_(n) (n=1, 2, 3, . . . ).

CPU 101 sets a threshold value for the initial form exponent S₀(hereinafter referred to as initial form exponent threshold value) (stepS105). As the initial form exponent threshold value may be used a valuewhich is empirically determined in advance or any numerical value inputto the input frame 5 of GUI 2 of FIG. 2. CPU 101 stores the set initialform exponent threshold value into the main memory 102. In thisembodiment, since a convex lesion candidate (polyp) is extracted, it isassumed that the initial form exponent threshold value represents thelower limit value.

CPU 101 calculates the initial form exponent S₀ for each pixel of thepanoramic image 71 created in step S103 by using the differentialdistance (parameter P1) set in step S104 (step S106).

CPU 101 executes threshold value processing on the form exponent S₀calculated in step S106 by using the initial form exponent thresholdvalue set in step S105 to extract a region falling into a thresholdvalue range, and stores the region into the main memory 102 (step S107).

Specifically, CPU 101 sets, as lesion candidate regions, pixels havingform exponents S₀ which are above the set initial form exponentthreshold value. Through these stages, in order to roughly extract thelesion candidate regions, it is desired to set the initial form exponentthreshold value set in step S105 to a relatively low value like “0.75”,for example (see FIG. 2).

At this stage, some regions 501, 502, 503, . . . in the panoramic imageare extracted like regions indicated by hatched lines of an image 711 ofFIG. 6. With respect to a convex surface whose size remarkably exceedsthe set parameter P1 (differential distance), the value of thecalculated initial form exponent S₀ is smaller, so that it is set as anout-of-target of lesion candidate and thus it is not extracted. All thehatched regions in FIG. 6 are regions extracted in the processing ofstep S107, however, reference numerals of some regions in FIG. 6 areomitted.

With respect to each region extracted in step S107, CPU 101 calculatesvarious kinds of feature amounts such as the degree of circularity, amajor-axis/minor-axis ratio, etc. of the region. With respect to thecalculated feature amounts, only regions falling into the presetthreshold value range are extracted, and false-positive regions aredeleted (step S108). The regions 501, 502, 503, 504, 507, 508, 512, 514remaining at this stage are shown in FIG. 7.

In the image 712 shown in FIG. 7, out of the extracted regions shown inFIG. 6, regions which are small in major-axis/minor-axis ratio and haveforms relatively close to a circle are extracted. For example, anaverage value <S₀> of the initial form exponents S₀ of the respectivepixels in the region 501 of FIG. 7 is assumed to represent “0.75”.

Subsequently, CPU 101 calculates the region size for each regionextracted in step S108 (step S109). In the following description, anumber i is affixed to an extracted lesion candidate region, the i-thlesion candidate region is referred to as a region i, and the regionsize of the region i is referred to as Li. The region size Li may be setto the maximum value of the distances among all the pixels belonging tothe end (contour) of the region I, for example, as shown in FIG. 8. CPU101 holds the region size Li calculated for each region i into the mainmemory 102.

Subsequently, CPU 101 resets a parameter di for each lesion candidateregion extracted in step S108 by using the region size Li calculated instep S109, and holds the parameter di into the main memory 102 (stepS110). The parameter di is a differential distance used to re-calculatethe form exponent S_(n), and it is calculated according to the followingmathematical expression (4), for example. α of the mathematicalexpression (4) represents a coefficient which is empirically determinedin advance.[Expression 4]di=αLi  (4)

Subsequently, CPU 101 resets the threshold value for the re-calculatedform exponent S_(n) (step S111). A value which is empirically determinedin advance may be used as the threshold value, and any numerical valueinput to the input frame 6 of GUI 2 of FIG. 2 may be used as thethreshold value. The threshold value set in step S111 is referred to asthe threshold value of the re-calculated form exponent S_(n). CPU 101holds the set threshold value of the re-calculated form exponent S_(n)into the main memory 102. In this case, it is assumed that a value of“0.9” is input to the input frame 6 as shown in FIG. 2.

CPU 101 re-calculates the form exponent S_(n) for each region extractedin step S108 by using the parameter di set in step S110 (step S112).Here, the form exponent S_(n) is calculated according to the abovemathematical expressions (1), (2) and (3). However, the parameter P1contained in the mathematical expression (3) is assumed to be replacedby the reset parameter di.

Here, CPU 101 may execute expansion processing on each lesion candidateregion extracted in step S108 and then re-calculate the form exponentS_(n). The expansion processing is the processing of expanding the edgeof the region i by the amount corresponding to one to several pixels.The region which has been subjected to the expansion processing is setas a calculation target of the form exponent S_(n), whereby the formexponent S_(n) can be re-calculated for even pixels which are excludedfor a reason such as nonconformity of the parameter P1 or the like atthe calculation stage (step S106) of the initial form exponent S₀,thereby enhancing the extraction precision. Not limited to the expansionprocessing, a region as a calculation target of the form exponent S_(n)may be arbitrarily expanded.

CPU 101 executes threshold value processing on the form exponent S_(n)calculated in step S112 by using the threshold value set in step S111,and extracts a region falling in the threshold value range (step S113).

At this stage, some regions 501, 502, 503, 504, 507, 508, 512 and 515are extracted in the panoramic image 713 like hatched regions of theimage 713 of FIG. 9. In the case of the region 501 as an example, theaverage value <S_(n)> of form exponents S_(n) of respective pixels inthe region 501 is corrected to “0.98” through the processing from stepS110 to step S113. The average value <S₀> of the initial form exponentsS₀ of the respective pixels in the corresponding region 501 of FIG. 7which are extracted at the stage before the processing from step S110 tostep S113 is executed is equal to “0.75”.

CPU 101 calculates various kinds of feature amounts such as the degreeof circularity, a major-axis/minor-axis ratio, etc. of a region for eachlesion candidate region extracted in step S113. With respect to thecalculated feature amounts, only regions falling in the preset thresholdvalue range are extracted, and false-positive regions are deleted (stepS114). The lesion candidate regions 501, 504, 507 remaining at thisstage are shown in FIG. 10.

CPU 101 re-calculates the region size Li for each lesion candidateregion i extracted in step S114, and holds it into the main memory 102(step S115). The region size Li is determined as in the case of the stepS109.

The processing from steps S110 to S115 may be executed only once orrepetitively executed at plural times. When the processing is repeatedat plural times, as shown in step S116, CPU 101 compares the region sizeof the lesion candidate region re-extracted in the previous loop withthe region size of the lesion candidate region re-extracted in thepresent loop, and shifts the processing to step S117 when the differencetherebetween is equal to a predetermined value or less.

In step S117, CPU 101 creates a superimposed image 715 obtained bysuperimposing each lesion candidate region extracted in step S114 on thepanoramic image 71 created in step S103. Each lesion candidate region ofthe superimposed image 715 is assumed to be supplied with a differentcolor value in accordance with the value of the form exponent S_(n)re-calculated in step S112 (step S117). CPU 101 displays thesuperimposed image 715 created in step S117 in the image display region7 within GUI 2 displayed on the display device 107 (step S108).

For example, in the superimposed image 715, the re-extracted lesioncandidate regions 501, 504, 507 are superimposed and displayed on thepanoramic image 71 as shown in FIG. 11. The lesion candidate regions501, 504 and 507 are different from one another in region size. However,the values of the re-calculated form exponents S_(n) thereof are equalto the set threshold value or more (for example, “0.9” or more), andthus they have substantially similarity forms. Furthermore, it isassumed that the lesion candidate regions 501, 504 and 507 havesubstantially the same form exponent S_(n) and thus are displayed withthe same color.

In step S117, the lesion candidate regions in which color values aresuperimposed may be set to all the lesion candidate regions extracted instep S114 or to lesion candidate regions whose region sizes are equal toor more than a predetermined region size. The region size of the lesioncandidate region to be displayed may be set in accordance with a valuewhich is input to the input frame 8 of GUI 2 of FIG. 2 by an operator.In this case, CPU 101 refers to the region size Li calculated in stepS115, supplies color values to the lesion candidate regions whose regionsizes Li are equal to or larger than the region size input to the inputframe 8, whereby they are superimposed and displayed.

A numeral value corresponding to a moving operation of the scroll bar 9is input to the input frame 8 shown in GUI 2 of FIG. 2. In the exampleshown in FIG. 2, “6” mm is input to the input frame 8, and thus only thelesion candidate regions having the region sizes Li of 6 mm or more areselected, and superimposed and displayed.

The created superimposed image 715 may be displayed so as to be slidableat a predetermined feeding width in the core line direction of thehollow organ. In this case, CPU 101 may control the feeding width so asto reduce the feeding width to the next frame when a displayed frame (apart of the superimposed image) contains a lesion candidate region andincrease the feeding width to the next frame when no lesion candidateregion is contained.

For example, FIG. 12 is a diagram showing two slide-displayed continuousframes arranged in the vertical direction, wherein (A) shows a portioncontaining no lesion candidate region and (B) shows a portion containinga lesion candidate region.

When the slide-display feeding width at the portion containing no lesioncandidate region is represented by A as shown in FIG. 12(A) and theslide-display feeding width at the portion containing the lesioncandidate region is represented by Δ′ as shown in FIG. 12(B), CPU 101controls the feeding width so that Δ is larger than Δ′ (Δ>Δ′). Asdescribed above, the slide-display is performed so that the feedingwidth at the portion containing the lesion candidate region is reduced,whereby more attention is paid to the portion containing the lesioncandidate region.

As described above, in the image processing system 1 according to thefirst embodiment, the medical image processing device 100 executes theprocessing of extracting a lesion candidate region from a medical image(panoramic image 71). In the lesion candidate extraction processing, CPU101 calculates the form exponent S₀ for each pixel of the overallpanoramic image 71 by using an initial differential distance (parameterP1), and subjects the calculated form exponent S₀ to the threshold valueprocessing to extract the lesion candidate region. Furthermore, CPU 101makes an evaluation of the size of the lesion candidate region and theother feature amounts to thereby delete false-positive regions.Thereafter, CPU 101 calculates the region size Li for each lesioncandidate region, and resets the parameter di (differential distance)corresponding to the region size Li. Then, CPU 101 re-calculates theform exponent S_(n) for each lesion candidate region by using the resetparameter di. Furthermore, CPU 101 executes the threshold valueprocessing on the re-calculated form exponent S_(n) and makes anevaluation of the size of the lesion candidate region and the otherfeature amounts, whereby the false-positive regions are deleted and thelesion candidate regions are re-extracted. Thereafter, CPU 101superimposes and displays the re-extracted lesion candidate region onthe panoramic image 71 in a display style (color value or the like)which is different every form exponent S_(n).

Accordingly, the optimum differential distance di corresponding to theregion size Li of the lesion candidate region is applied so that theform of each lesion candidate region can be estimated. Therefore, withrespect to even lesion candidate regions having the same form anddifferent sizes, the lesion candidate regions concerned can be extractedat a time through a series of processing, and superimposed and displayedon the panoramic image. Furthermore, they are superimposed and displayedin the display style (color value) or the like which is different inaccordance with the form, and thus the lesion candidate regions can bedisplayed in the same display style even when they are different in sizefrom one another, but they have the similarity form, so that the lesioncandidates can be easily observed.

In the above example, the threshold value used in the thresholdprocessing of the form exponents S₀, S_(n) is set as the lower limitvalue. However, it may be set as the upper limit value or the range inaccordance with the form to be extracted. Furthermore, in thefalse-positive deletion processing of steps S108 and S114, themajor-axis/minor-axis ratio and the degree of circularity are estimatedas feature amounts, however, the present invention is not limited tothem. CT values, etc. of a region of interest may be set as featureamounts, and false-positive regions may be determined on the basis ofthese feature amounts.

Second Embodiment

Next, the image processing system 1 according to the second embodimentwill be described. In the second embodiment, a method of extracting alesion candidate region described with reference to the first embodimentis applied to a virtual endoscopic image. Furthermore, the hardwareconstruction of the image processing system 1 according to the secondembodiment is the same as the image processing system 1 according to thefirst embodiment of FIG. 1, and the description thereof is omitted. Thesame parts are represented by the same reference numerals.

The lesion candidate extraction processing executed in the medical imageprocessing device 100 according to the second embodiment will bedescribed.

CPU 101 of the medical image processing device 100 according to thesecond embodiment reads out a program and data concerning the lesioncandidate extraction processing shown in FIG. 13 from the main memory102, and executes the lesion candidate extraction processing on thebasis of the program and the data.

In the lesion candidate extraction processing of FIG. 13, as in the caseof the steps S101 to S102 of the lesion candidate extraction processing(FIG. 3) in the first embodiment, when the data read-in button 3 of GUI2 of FIG. 2 is clicked, CPU 101 executes the read-in processing of imagedata. CPU 101 reads out the selected image data from the storage device103 and holds the image data into the main memory 102 (step S201).Furthermore, CPU 101 extracts a core line from the read-in image data(step S202).

Subsequently, CPU 101 creates a display image by using the core lineinformation extracted in step S202. In this case, it is assumed that avirtual endoscopic image 72 is created as a display image (step S203;see FIG. 18). The virtual endoscopic image 72 is defined as an imageobtained when an aspect which is viewed from any viewing point set in ahollow organ region with some range of direction set as a visual fieldangle is projected onto a planar projection plane. The detailed creationof the virtual endoscopic image 72 is disclosed in Patent Document(JP-A-7-296184) or the like, and the description thereof is omitted.

CPU 101 sets the parameter P1 for calculating the initial form exponentS₀ for the virtual endoscopic image 72 created in step S203 (step S204).Here, as in the case of the step S104 of the first embodiment, the setparameter P1 is a differential distance for determining a differentvalue at a point of interest, for example. A value which is empiricallydetermined in advance may be used as the parameter P1, or any numericalvalue input to the input frame 4 of GUI 2 of FIG. 2 may be used as theparameter P1. CPU 101 stores the set parameter P1 into the main memory102.

Subsequently, as in the case of the step S105 of the first embodiment,CPU 101 sets the initial form exponent threshold value (step S205).

CPU 101 calculates the form exponent S₀ for each pixel of the overallvirtual endoscopic image 7 created in step S203 by using thedifferential distance (parameter P1) set in step S204 (step S206). Avalue represented by the above mathematical expression (1) is used asthe initial form exponent S₀ as in the case of the first embodiment.

CPU 101 executes the threshold value processing on the form exponent S₀calculated in step S206 by using the initial form exponent thresholdvalue set in step S205, and extracts regions falling in the thresholdvalue range (step S207).

At this stage, some lesion candidate regions 801, 802, 803, . . . in thevirtual endoscopic image 72 are extracted as shown in the image 721 ofFIG. 14. CPU 101 calculates the various kinds of feature amounts such asthe degree of circularity, the major-axis/minor-axis ratio, etc. of theregion for each lesion candidate region extracted in step S207. Withrespect to the feature amounts, only the regions falling in the presetthreshold value range are extracted, and false-positive regions aredeleted (step S208). Lesion candidate regions 801, 802, 803, 804, 806remaining at this stage are shown in FIG. 15.

In the example shown in FIG. 15, regions 801, 802, 803, 804 and 806which are small in major-axis/minor-axis ratio and relatively near to acircle in form are extracted out of the regions shown in FIG. 14. Forexample, it is assumed that the average value <S₀> of the initial formexponents S₀ of the respective pixels in the region 802 of FIG. 15represents “0.75”.

Subsequently, CPU 101 calculates the size (region size Li) for eachregion extracted in step S208 (step S209). The calculation of the regionsize Li is the same as the first embodiment. CPU 101 holds the regionsize Li calculated for each region into the main memory 102.

Subsequently, CPU 101 resets the parameter di of each lesion candidateregion extracted in step S208 by using the region size Li calculated instep S209, and holds the parameter di into the main memory 102 (stepS210). The parameter di is determined by using the above mathematicalexpression (4) as in the case of the first embodiment, and it is set tothe value corresponding to the region size Li of each lesion candidateregion i.

Subsequently, as in the case of the step S111 of the first embodiment,CPU 101 resets the threshold value for the re-calculated form exponentS_(n) (step S211). Furthermore, as in the case of the step S112 of thefirst embodiment, CPU 101 re-calculates the form exponent S_(n) for eachregion extracted in step S208 by using the parameter di set in the stepS210 (step S212).

Furthermore, as in the case of the step S113 of the first embodiment,CPU 101 executes the threshold value processing on the form exponentS_(n) re-calculated in step S212 by using the threshold value set instep S211, and extracts regions falling in the threshold range (stepS213).

At this stage, some regions 801, 802, 803, 804 and 806 are extracted inthe virtual endoscopic image 72 like hatched regions of the image 723 ofFIG. 16. Taking the region 802 as an example, the average value <S_(n)>of the form exponents S_(n) of the respective pixels in the region 802is corrected to “0.98” through the processing from step S210 to stepS213. The average value <S₀> of the initial form exponents S₀ of therespective pixels in the corresponding region 802 of FIG. 15, which isextracted at the stage before the processing from step S210 to stepS213, is equal to “0.75”.

As in the case of the step S114 of the first embodiment, CPU 101calculates the various kinds of feature amounts of a region such as thedegree of circularity, the major-axis/minor-axis ratio, etc. for eachregion extracted in step S213. With respect to the calculated featureamounts, only regions falling in the preset threshold value range areextracted, and false-positive regions are deleted (step S214). Theregions 801, 802 and 803 remaining at this stage are shown in FIG. 17.

CPU 101 re-calculates the region size Li for each lesion candidateregion i re-extracted in step S214. The region size Li is determined asin the case of the step S209.

As in the case of the first embodiment, the processing of steps S210 toS215 may be executed only once or repeated at plural times. When it isrepeated at plural times, as indicated in the step S216, the region sizeof the lesion candidate region re-extracted in the previous loop iscompared with the region size of the lesion candidate regionre-extracted in the present loop, and when the difference therebetweenis equal to a predetermined value or less, the processing shifts to stepS217.

In step S217, CPU 101 creates a superimposed image 725 obtained bysuperimposing each lesion candidate region extracted in step S214 on thevirtual endoscopic image 72 created in step S203. It is assumed that acolor value which is different in accordance with the value of the formexponent S_(n) re-calculated in step S212 is given to each lesioncandidate region of the superimposed image 725 (step S217). Then, CPU101 displays the superimposed image 725 created in step S217 on theimage display region 7 in GUI 2 (step S218).

For example, in the superimposed image 725, the re-extracted lesioncandidate regions 801, 802 and 803 are displayed on the virtualendoscopic image 72 as shown in FIG. 18. The lesion candidate regions801, 802 and 803 are different in region size, however, the values ofthe re-calculated form exponents S_(n) thereof are equal to a setthreshold value or more (for example, “0.9” or more), so that they havesubstantially similarity forms. Furthermore, the lesion candidateregions 801, 802 and 803 have substantially the same form, and thus theyare displayed with the same color.

As in the case of the first embodiment, in step S217, the lesioncandidate regions on which the color values are superimposed may beapplied to all the lesion candidate regions extracted in step S214.However, they may be applied to only regions having a predeterminedregion size or more out of the above lesion candidate regions.

Furthermore, as in the case of the first embodiment, the createdsuperimposed image 725 may be slide-displayed at a predetermined feedingwidth in the core line direction of the hollow organ. In this case, CPU101 may control the feeding width so that the feeding width to the nextframe is reduced when the displayed frame (a part of the superimposedimage) contains a lesion candidate region, and the feeding width to thenext frame is increased when the displayed frame contains no lesioncandidate region.

For example, FIG. 19 is a diagram showing two slide-displayed continuousframes, wherein (A) shows a portion containing no lesion candidateregion, and (B) shows a portion containing a lesion candidate region.

As shown in FIG. 19(A), the corresponding points of the continuousframes 726 and 727 containing no lesion candidate region are representedby 726 a, 727 a. Furthermore, as shown in FIG. 19(B), the correspondingpoints of the continuous frames 728, 729 containing a lesion candidateregion are represented by 728 a, 729 a. In this case, CPU 101 controlsto increase the movement amount of a viewing point more greatly at theportion containing no lesion candidate region of FIG. 19(A) as comparedwith the portion containing a lesion candidate region of FIG. 19(B). Asdescribed above, the slide-display is executed while the feeding widthof the portion containing the lesion candidate region is reduced,whereby more attention is paid to the portion containing the lesioncandidate region.

As described above, according to the second embodiment, the sameprocessing as the first embodiment (extraction of the lesion candidateregion from the panoramic image) is executed on the virtual endoscopicimage 72.

Accordingly, with respect to even the virtual endoscopic image, lesioncandidate regions which have the same form and different sizes can beextracted at a time through a series of processing, and superimposed anddisplayed.

Third Embodiment

Next, the image processing system 1 according to a third embodiment willbe described. The hardware construction of the image processing system 1according to the third embodiment is the same as the image processingsystem 1 according to the first embodiment of FIG. 1, and thus thedescription thereof is omitted. The same parts are represented by thesame reference numerals and described.

In the third embodiment, the lesion candidate regions extracted from thepanoramic image 71 in the lesion candidate extracting processing (thesteps S101 to S117 of FIG. 3) of the first embodiment are reflected tothe virtual endoscopic image 72.

The image processing system 1 according to the third embodiment will bedescribed hereunder with reference to FIGS. 20 and 21.

In the display processing of the third embodiment shown in FIG. 20, CPU101 first extracts a lesion candidate region from a panoramic image 71in the steps S101 to S116 of the lesion candidate extraction processingof FIG. 3, and also stores a re-calculated form exponent S_(n) (stepS112 of FIG. 3) into the main memory 102 (step S301). The color valuesuperimposition processing of the step S117 and the superimpositionimage display processing of the step S118 in FIG. 3 may be omitted.

Furthermore, CPU 101 acquires, for example, coordinate information suchas a real space coordinate or the like for the lesion candidate regionextracted in step S301 and holds it into the main memory 102 (stepS302).

Subsequently, CPU 101 creates the virtual endoscopic image 72 accordingto the processing of the steps S201 to S203 of FIG. 13 (step S303).Then, CPU 101 determines whether the coordinate corresponding to thecoordinate information obtained in step S302 (the real space coordinateof the lesion candidate region extracted from the panoramic image 71) iscontained in the real space coordinate of the inner wall displayed onthe virtual endoscopic image 72 created in step S303 (step S304).

When it is determined in step S304 that the coordinate corresponding tothe coordinate information (lesion candidate region) obtained in stepS302 is contained in the real space coordinate of the inner walldisplayed on the virtual endoscopic image 72, CPU 101 creates asuperimposed image 732 superimposed with the color value representingthe lesion candidate region (see FIG. 21) at the correspondingcoordinate of the inner wall of the virtual endoscopic image 72. Here,the color value representing the lesion candidate region is set to thecolor value corresponding to the form exponent S_(n) of each regionstored in the main memory 102 in step S301 (step S305).

CPU 101 displays the superimposed image 732 created in step S305 in theimage display region 7 in the GUI 2 shown in FIG. 2 (step S306). Here,it is desired that both the superimposed image 731 onto the panoramicimage 71 and the superimposed image 732 onto the virtual endoscopicimage 72 are displayed in the image display region 7. When both thesuperimposed image 731 onto the panoramic image 71 and the superimposedimage 732 onto the virtual endoscopic image 72 are displayed, thecomparison of the lesion candidates can be easily performed, and thusreading can be further efficiently performed.

When the real space coordinates corresponding to 501 a, 507 a in thelesion candidate regions 501 a, 504 a, 507 a in the superimposed image731 in the panoramic image 71 are within the virtual endoscopic image 72as shown in FIG. 21, the corresponding regions 501 b, 507 b aredisplayed at the corresponding coordinate positions.

As described above, according to the third embodiment, the imageprocessing device 100 superimposes and displays the lesion candidateregion extracted in the panoramic image 71 at the corresponding positionof the virtual endoscopic image 72. As a result, the comparison readingof the lesion candidate region between the panoramic image 71 and thevirtual endoscopic image 72 can be easily performed and thus thediagnosis efficiency is enhanced.

As in the case of the first and second embodiments, in the step S305,all the lesion candidate regions extracted in step S301 or only thelesion candidate regions having larger region sizes than a predeterminedregion size may be set as the lesion candidate regions on which thecolor values are superimposed. Furthermore, in the third embodiment, thelesion candidate region extracted from the panoramic image 71 isreflected to the virtual endoscopic image 72. However, conversely, thelesion candidate region extracted from the virtual endoscopic image 72may be reflected to the panoramic image 71, or the lesion candidateregion extracted from the panoramic image 71 or the virtual endoscopicimage 72 may be reflected to the medical tomographic image.

Fourth Embodiment

In a fourth embodiment, various display styles of the lesion candidateregion extracted according to the methods described with reference tothe first to third embodiments will be described.

As shown in FIG. 22, CPU 101 first extracts the lesion candidate regionfrom the panoramic image 71 or the virtual endoscopic image 72 (stepS401). The extraction of the lesion candidate region is the same as theprocessing of the steps S101 to S116 of FIG. 3 or the steps S201 to S216of FIG. 13, and the description thereof is omitted.

Subsequently, CPU 101 calculates the region size Li for each lesioncandidate region i extracted in step S401 (step S402). The calculationof the region size Li is the same as the step S109 of FIG. 3, the stepS209 of FIG. 13 or the like. CPU 101 classifies the respective regions iinto plural classes such as three stages or the like on the basis of theregion size Li calculated in the step S402 (step S403).

CPU 101 creates a superimposed image in the display style (for example,color value, transparency, pattern or the like) corresponding to a classin which each lesion candidate region extracted in step S401 isclassified in step S403 (step S404), and displays the createdsuperimposed image on the display screen (step S405).

As the display style corresponding to the classified class, for example,with respect to lesion candidate regions 501 c, 504 c, 507 c belongingto different classes displayed on the panoramic image 741 and lesioncandidate regions 801 c, 802 c, 803 c belonging to different classesdisplayed on the virtual endoscopic image 742, the lesion candidateregions having different region sizes are displayed with differentcolors, for example, like red, blue and yellow as shown in FIG. 23. Insuch a case, an indication such as the degree of risk or the like whichis estimated form the region size can be easily determined.

Furthermore, as indicated by 501 d, 504 d, 507 d, 801 d, 802 d, 803 d ofFIG. 24, colors such as red, blue, yellow which are allocated everyclass may be applied to only the edges of the respective lesioncandidate regions. When only the edges of the regions are colored asdescribed above, the surface state of the lesion candidate region can beobserved.

As indicated by 501 e, 504 e, 507 e, 801 e, 802 e, 803 e of FIG. 25, thetransparency of coloring may be varied every class. For example, classesin such a level that a lesion candidate region has a large region sizeand thus overlooking causes a risk is colored with an opaque color so asto make it conspicuous. With respect to classes which have small sizesand thus have a small degree of risk, the transparency is increased orthe like. When the transparency is varied every class as describedabove, more attention is paid to a region having a large size and thus alarge degree of risk.

As described above, in the image processing system 1 according to thefourth embodiment, the lesion candidate regions extracted from themedical image are classified into plural classes on the basis of theregion size, and they are displayed on the medical image in differentdisplay styles in accordance with the classified classes. As a result,the degree of risk of lesion can be easily determined on the basis ofthe difference in display style.

In the fourth embodiment, the grouping (classification) based on theregion size of the lesion candidate region is executed, however, thisembodiment is not limited to this classification. For example,classification based on the form such as the form exponent or the like,or classification based on other feature amounts may be adopted.Furthermore, in the first to fourth embodiments, extraction of a lesioncandidate region on the inner wall of a colon has been described.However, not only other hollow organs such as bronchial tubes, bloodvessels, small intestine, etc., but also digestive organs such asstomach, etc., prominences at the outside of hollow organs such asaneurysm, etc. may be targeted.

The method described with reference to the first to fourth embodimentsmay be arbitrarily combined. Furthermore, it is apparent that variousmodifications or alterations may be made by persons skilled in the artwithin the scope of the technical idea disclosed in this application,and it is understood that they belong to the technical scope of thisinvention.

DESCRIPTION OF REFERENCE NUMERALS

-   1 image processing system, 100 medical image processing device, 101    CPU 101, 102 main memory, 103 storage device, 104 communication IF,    105 display memory, 106 I/F, 107 display device, 108 mouse (external    equipment), 109 input device, 110 network, 111 image data base, 112    medical image scanning device, 2 GUI, 4 initial differential    distance input frame, 5 initial form exponent threshold value input    frame, 6 form exponent threshold value input frame, 7 image display    region, 8 size input frame, 9 scroll bar, 71 panoramic image, 715    superimposed image onto panoramic image, 501 to 515 lesion candidate    region, 72 virtual endoscopic image, 725 superimposed image onto    virtual endoscopic image, 801 to 808 lesion candidate region

The invention claimed is:
 1. A medical image processing device forextracting from a medical image, and displaying, lesion candidateregions on a surface of an organ, the medical image processing devicecomprising: a first extracting unit that makes a first evaluation of acurved surface form of the organ by using an initial parameter, andextracts first lesion candidate regions from volume image data based onthe first evaluation; a second extracting unit that makes a secondevaluation of a curved surface form of the organ by using a parameterreset based on a region size of each of the first lesion candidateregions, and re-extracts second lesion candidate regions from volumeimage data based on the second evaluation; and a display unit thatsuperimposes and displays the second lesion candidate regions extractedby the second extracting unit on a second medical image.
 2. The medicalimage processing device according to claim 1, characterized in that thefirst extracting unit and the second extracting unit make the firstevaluation and the second evaluation by using a form exponent forestimating a state of the curved surface form.
 3. The medical imageprocessing device according to claim 2, characterized in that the firstextracting unit and the second extracting unit calculate the formexponent by using a predetermined parameter.
 4. The medical imageprocessing device according to claim 3, characterized in that the formexponent is Shape Index, and the predetermined parameter is adifferential distance for determining a differential value at a point ofinterest.
 5. The medical image processing device according to claim 3,characterized in that the second extracting unit calculates the formexponent by using a differential distance corresponding to a region sizeof each of the first lesion candidate regions.
 6. The medical imageprocessing device according to claim 1, characterized in that the firstextracting unit and the second extracting unit calculate a predeterminedfeature amount for the extracted lesion candidate regions, and byextracting regions in which the calculated feature amount falls into apredetermined threshold value range, delete a false-positive region fromthe extracted lesion candidate regions.
 7. The medical image processingdevice according to claim 6, characterized in that the second extractingunit executes expansion processing on regions obtained by deleting thefalse-positive region from the first lesion candidate regions to makethe second evaluation.
 8. The medical image processing device accordingto claim 1, characterized in that the first medical image and the secondmedical image are panoramic images of a hollow organ region created byusing volume image data.
 9. The medical image processing deviceaccording to claim 1, characterized in that the first medical image andthe second medical image are virtual endoscopic images of a hollow organregion created by using volume image data.
 10. The medical imageprocessing device according to claim 1, characterized in that the firstmedical image is a panoramic image of a hollow organ region created byusing volume image data, and the second medical image is a virtualendoscopic image of the hollow organ region created by using the volumeimage data.
 11. The medical image processing device according to claim1, characterized in that the display unit further superimposes anddisplays the second lesion candidate regions extracted by the secondextracting unit on the second medical image in a display stylecorresponding to a form of the second lesion candidate region.
 12. Themedical image processing device according to claim 1, characterized inthat the display unit further superimposes and displays the secondlesion candidate regions extracted by the second extracting unit on thesecond medical image in a display style corresponding to a region sizeof the second lesion candidate region.
 13. The medical image processingdevice according to claim 1, further comprising an specifying unit thatspecifies a range concerning the region size of the lesion candidateregion to be superimposed and displayed on the second medical image,wherein the display unit further superimposes and displays, on thesecond medical image, a second lesion candidate region having a regionsize within the range specified by the specifying unit out of the secondlesion candidate regions extracted by the second extracting unit.
 14. Amedical image processing method for extracting from a medical image, anddisplaying, lesion candidate regions on a surface of an organ, themedical image processing method comprising: a first extracting step thatmakes a first evaluation of a curved surface form of the organ by usingan initial parameter, and extracts first lesion candidate regions fromvolume image data based on the first evaluation; a second extractingstep that makes a second evaluation of a curved surface form of theorgan by using a parameter reset based on a region size of each of thefirst lesion candidate regions, and re-extracts second lesion candidateregions from volume image data based on the second evaluation; and adisplay step that superimposes and displays the second lesion candidateregions, extracted in the second extraction step, on a second medicalimage.
 15. The medical image processing method according to claim 14,characterized in that the display step further superimposes and displaysthe second lesion candidate regions extracted by the second extractingstep in a display style corresponding to a form or region size of thesecond lesion candidate.